Cancer Res Treat.  2024 Apr;56(2):343-356. 10.4143/crt.2023.1302.

Applicability of Spatial Technology in Cancer Research

• Ahn S
• Lee HS

• KMID: 2554327
• DOI: http://doi.org/10.4143/crt.2023.1302

Abstract

This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.

Keyword

Spatial transcriptomics; Spatial proteomics; Tumor microenvironment; Cancer research

Figure

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  Fig. 1. Capture-based technologies. (A) Visium utilizes an array of 55-μm-diameter spots where transcriptome primers are arranged with locational information (denoted by yellow, orange, and green color in the circle of the left picture). Next, the overlying tissue is eliminated, and cDNA library preparation ensues. Subsequently, the transcriptome information, along with locational data, is aligned onto a scanned histology image. (B) In GeoMx Digital Spatial Profiler (DSP), the morphological guidance of fluorescently tagged antibodies staining (not shown in this figure) aids in the collection of oligonucleotide within user-defined regions of interest. Transcriptome data obtained through microcapillaries undergoes sequencing. NGS, next-generation sequencing.

• 

  Fig. 2. Image-based technologies. (A) In the in-situ hybridization–based method, individual RNA species are detected and released as signals, as depicted by red or grey dots, subsequently to be decryped through a pre-defined barcode scheme where they are labeled as “red” and assigned the value “1” (“on-off imaging signals”). Following the iteration of these steps, high-throughput RNA spatial profiling can be attained with subcellular resolution. (B) The in-situ sequencing–based method employs a padlock probe (illustrated as blue circle) to amplify the target sequence, resulting in rolling circle amplification (RCA). cDNA libraries are generated through DNA probe binding (depicted as blue, red, yellow, and green), and subsequently subjected to sequencing.

• 

  Fig. 3. Antibody-based technologies. (A) Enzyme-tagged antibodies are iteratively labeled, imaged, and removed. These signals are amplified using tyramide-activated via horseradish peroxidase conjugated on the primary antibody (denoted as “w/ TSA” in the top left box). (B) A cocktail of metal-isotope–tagged antibodies are used to simultaneously detect multiple target proteins, thus referred as “one-shot technology.” MS, mass-spectrometry. (C) DNA barcoding–based multiplexing which employs a cocktail of oligo-tagged primary antibodies, are utilized to visualize the targets of interest. The cyclic process, involving DNA annealing and dehybridization, is iterated until all antibody targets are visualized through annealing.

Reference

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